Cutter tool automatic identification and management method and system based on image identification

Abstract

The invention discloses an automatic identification and management method and system for a cutter tool based on image identification, and the method comprises the steps: obtaining a to-be-identified cutter tool image, and extracting a plurality of features of the to-be-identified cutter tool image, the plurality of features including the shape feature of the cutter tool, the material feature of the cutter tool, and the wear state feature of the cutter tool; the method comprises the following steps: respectively setting a shape feature mapping function, a material feature mapping function and a wear state mapping function, and according to the shape feature of the cutter tool, the material feature of the cutter tool and the wear state feature of the cutter tool, determining the wear state of the cutter tool; calculating a mapping value of the shape characteristic of the cutter tool, a mapping value of the material characteristic of the cutter tool and a mapping value of the wear state characteristic of the cutter tool; and according to the mapping value of the shape feature of the cutter tool, the mapping value of the material feature of the cutter tool and the mapping value of the wear state feature of the cutter tool in the to-be-identified cutter tool image, finding out the most matched cutter tool in the database as an identification result of the to-be-identified cutter tool image.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing, and more specifically, relates to an automatic identification and management method and system for knife tools based on image recognition. Background Art

[0002] Knife security checks at airports and train stations are an important part of ensuring passenger safety. Security checks usually include the following steps:

[0003] X-ray inspection: The luggage is scanned through an X-ray machine, and security personnel will check the items in the luggage to see if there are knives or other prohibited items.

[0004] Manual inspection: If the X-ray image shows abnormalities, the security personnel will ask you to open the bag and carefully check the suspicious items in the baggage.

[0005] Metal Detection: When passengers pass through the security gate, they will pass through a metal detector to detect whether they are carrying metal knives. If the detector alarms, the security personnel will use a handheld metal detector for further inspection.

[0006] Handling of knives and prohibited items: If knives or other prohibited items are found, security personnel will handle them according to regulations. Small knives may be confiscated or passengers may be asked to dispose of them themselves (such as mailing or giving them away). Large knives and other dangerous items are generally not allowed on transportation.

[0007] At present, security checks on knife tools at airports or train stations still rely mainly on manual judgment, which results in some tools that could have been brought in being unable to be brought along. Summary of the invention

[0008] In order to solve the above technical problems, the present invention proposes an automatic identification and management method for knife tools based on image recognition, comprising:

[0009] Acquire a knife tool image to be identified, and extract multiple features of the knife tool image to be identified, wherein the multiple features include: shape features of the knife tool, material properties of the knife tool, and wear state features of the knife tool;

[0010] Respectively setting a shape feature mapping function, a material property mapping function and a wear state mapping function, and calculating a mapping value of the shape feature of the knife tool, a mapping value of the material property of the knife tool and a mapping value of the wear state characteristic of the knife tool according to the shape feature of the knife tool, the material property of the knife tool and the wear state characteristic of the knife tool;

[0011] The mapping values ​​of the shape features of the knife tool in the knife tool image to be identified, the mapping values ​​of the material properties of the knife tool and the mapping values ​​of the wear state features of the knife tool are matched with the knife tools already in the database, and the best matching knife tool in the database is found as the recognition result of the knife tool image to be identified.

[0012] Furthermore, the shape feature mapping function used to describe the shape change of the knife tool includes:

[0013]

[0014] Among them, f(S) is the mapping value of the shape feature S of the tool, m is the number of shape features, α i is the first adjustment factor of the i-th shape feature, β i is the second adjustment factor of the i-th shape feature, S i is the i-th shape feature, γ i is the third adjustment factor of the i-th shape feature, δ i is the fourth adjustment factor of the i-th shape feature, ∈ i is the fifth adjustment factor of the i-th shape feature, ξ i is the sixth adjustment factor of the i-th shape feature, η ij To control the i-th shape feature S i and the j-th shape feature S j The adjustment factor for the interaction between j is the adjustment factor of the j-th shape feature.

[0015] Furthermore, the mapping function of the material properties used to describe the material properties of the knife tool includes:

[0016]

[0017] Among them, g(M) is the mapping value of the material property M of the tool, n is the number of material properties, λ′ i is the first adjustment factor of the i-th material property, η′ i is the second adjustment factor of the i-th material property, M i is the i-th material property, M j is the jth material property, κ ij To control the i-th material property M i and the jth material property M j The adjustment factor for the interaction between i is the third adjustment factor of the i-th material property, ρ i is the fourth adjustment factor of the i-th material property, σ i is the fifth adjustment factor for the i-th material property.

[0018] Furthermore, the wear state mapping function used to describe the wear condition of the tool includes:

[0019]

[0020] Where h(W) is the mapping value of the wear state feature W of the tool, p is the number of wear state features, θ j is the first adjustment factor of the jth wear state characteristic, ρ′ j is the second adjustment factor of the jth wear state characteristic, τ j is the third adjustment factor of the jth wear state characteristic, W j is the jth wear state characteristic, σ′ j is the fourth adjustment factor of the jth wear state characteristic, ω j is the fifth adjustment factor of the jth wear state characteristic, t is time, ζ j is the sixth adjustment factor of the jth wear state characteristic, χ j is the seventh adjustment factor of the hth wear state characteristic.

[0021] Furthermore, all adjustment factors are fitted by gradient descent method or ant colony algorithm.

[0022] The present invention also proposes an automatic identification and management system for knife tools based on image recognition, comprising:

[0023] A feature acquisition module is used to acquire a knife tool image to be identified and extract multiple features of the knife tool image to be identified, wherein the multiple features include: shape features of the knife tool, material properties of the knife tool and wear state features of the knife tool;

[0024] a mapping value calculation module, used to respectively set a shape feature mapping function, a material property mapping function and a wear state mapping function, and calculate a mapping value of the shape feature of the knife tool, a mapping value of the material property of the knife tool and a mapping value of the wear state characteristic of the knife tool according to the shape feature of the knife tool, the material property of the knife tool and the wear state characteristic of the knife tool;

[0025] The recognition module is used to match the mapping values ​​of the shape characteristics of the knife tool of the knife tool image to be recognized, the mapping values ​​of the material properties of the knife tool and the mapping values ​​of the wear state characteristics of the knife tool with the knife tools already in the database, and find the most matching tool in the database as the recognition result of the knife tool image to be recognized.

[0026] Furthermore, the shape feature mapping function used to describe the shape change of the knife tool includes:

[0027]

[0028] Among them, f(S) is the mapping value of the shape feature S of the tool, m is the number of shape features, α i is the first adjustment factor of the i-th shape feature, β i is the second adjustment factor of the i-th shape feature, S i is the i-th shape feature, γ i is the third adjustment factor of the i-th shape feature, δ i is the fourth adjustment factor of the i-th shape feature, ∈ i is the fifth adjustment factor of the i-th shape feature, ξ i is the sixth adjustment factor of the i-th shape feature, η ij To control the i-th shape feature S i and the j-th shape feature S j The adjustment factor for the interaction between j is the adjustment factor of the j-th shape feature.

[0029] Furthermore, the mapping function of the material properties used to describe the material properties of the knife tool includes:

[0030]

[0031] Among them, g(M) is the mapping value of the material property M of the tool, n is the number of material properties, λ′ i is the first adjustment factor of the i-th material property, η′ i is the second adjustment factor of the i-th material property, M i is the i-th material property, M j is the jth material property, κ ij To control the i-th material property M i and the jth material property M j The adjustment factor for the interaction between i is the third adjustment factor of the i-th material property, ρ i is the fourth adjustment factor of the i-th material property, σ i is the fifth adjustment factor for the i-th material property.

[0032] Furthermore, the wear state mapping function used to describe the wear condition of the tool includes:

[0033]

[0034] Where h(W) is the mapping value of the wear state feature W of the tool, p is the number of wear state features, θ j is the first adjustment factor of the jth wear state characteristic, ρ′ jis the second adjustment factor of the jth wear state characteristic, τ j is the third adjustment factor of the jth wear state characteristic, W j is the jth wear state characteristic, σ′ j is the fourth adjustment factor of the jth wear state characteristic, ω j is the fifth adjustment factor of the jth wear state characteristic, t is time, ζ j is the sixth adjustment factor of the jth wear state characteristic, χ j is the seventh adjustment factor of the jth wear state characteristic.

[0035] Furthermore, all adjustment factors are fitted by gradient descent method or ant colony algorithm.

[0036] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0037] The present invention can accurately identify knife tools through the above technical solution, thereby identifying dangerous knife tools or knife tools that can pass security inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0039] Figure 2 It is a structural diagram of the system of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0040] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0041] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0042] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0043] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0044] The display screen is used to display the user interface of each application.

[0045] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0046] Example 1

[0047] like Figure 1 As shown, an embodiment of the present invention provides a method for automatic identification and management of knife tools based on image recognition, comprising:

[0048] Step 101, obtaining a tool image to be identified, and extracting multiple features of the tool image to be identified, wherein the multiple features include: a shape feature of the tool, a material property of the tool, and a wear state feature of the tool;

[0049] For example, 1. The shape feature vector S mainly describes the geometric shape of the tool and may include the following features:

[0050] S1-Blade length: The blade length of the tool, usually in millimeters (mm).

[0051] S2-Blade Angle: The angle of the cutting edge of a tool, usually measured in degrees (°).

[0052] S3-Tool Width: The width of the tool, usually in millimeters (mm).

[0053] S4-Tool Thickness: The thickness of the tool, usually in millimeters (mm).

[0054] S5-Blade Curvature Radius: The radius of curvature of the blade, usually measured in millimeters (mm).

[0055] S6-Edge shape parameters: Parameters used to describe the shape of the tool edge, such as sharpness or shape factor.

[0056] 2. The material property vector M describes the material properties of the tool and may include the following characteristics:

[0057] M1-Hardness: The hardness of the tool material, such as Vickers hardness (HV) or Rockwell hardness (HRC).

[0058] M2 - Modulus of elasticity: The modulus of elasticity of a material, usually expressed in GPa.

[0059] M3-Density: The density of the material, usually expressed in grams per cubic centimeter (g / cm3 ) as the unit.

[0060] M4- Thermal conductivity: The thermal conductivity of the material, usually in W / m·K.

[0061] M5-Toughness: The toughness of a material is usually measured by fracture toughness (K_IC), with the unit of MPa·√m.

[0062] M6-Chemical composition: The chemical composition ratio of the material, such as the percentage of carbon content or other alloying elements.

[0063] 3. The wear state vector W describes the wear of the tool and may include the following characteristics:

[0064] W1-Edge wear: The wear of the tool edge, usually in microns (μm).

[0065] W2-Degree of cutting edge chipping: The severity of cutting edge chipping may be a dimensionless quantitative value.

[0066] W3-Surface roughness: The roughness of the tool surface after wear, usually in microns (μm).

[0067] W4- Wear area: The total area of ​​tool surface wear, usually in square millimeters (mm 2 ) as the unit.

[0068] W5- Thermal damage index: The degree of damage to the tool due to overheating, which can be an empirical value or a dimensionless parameter.

[0069] W6-Chip adhesion degree: The degree of chip adhesion to the tool surface, usually expressed as a ratio.

[0070] Step 102, respectively setting a shape feature mapping function, a material property mapping function and a wear state mapping function, and calculating a mapping value of the shape feature of the knife tool, a mapping value of the material property of the knife tool and a mapping value of the wear state characteristic of the knife tool according to the shape feature of the knife tool, the material property of the knife tool and the wear state characteristic of the knife tool;

[0071] Specifically, the shape feature mapping function used to describe the shape change of the knife tool includes:

[0072]

[0073] Among them, f(S) is the mapping value of the shape feature S of the tool, m is the number of shape features, α i is the first adjustment factor of the i-th shape feature, β i is the second adjustment factor of the i-th shape feature, S iis the i-th shape feature, γ i is the third adjustment factor of the i-th shape feature, δ i is the fourth adjustment factor of the i-th shape feature, ∈ i is the fifth adjustment factor of the i-th shape feature, ξ i is the sixth adjustment factor of the i-th shape feature, η ij To control the i-th shape feature S i and the j-th shape feature S j The adjustment factor for the interaction between j is the adjustment factor of the j-th shape feature.

[0074] Specifically, the mapping function of the material properties is used to describe the material properties of the tool and includes:

[0075]

[0076] Among them, g(M) is the mapping value of the material property M of the tool, n is the number of material properties, λ′ i is the first adjustment factor of the i-th material property, η′ i is the second adjustment factor of the i-th material property, M i is the i-th material property, M j is the jth material property, κ ij To control the i-th material property M i and the jth material property M j The adjustment factor for the interaction between i is the third adjustment factor of the i-th material property, ρ i is the fourth adjustment factor of the i-th material property, σ i is the fifth adjustment factor for the i-th material property.

[0077] Specifically, the wear state mapping function used to describe the wear condition of the tool includes:

[0078]

[0079] Where h(W) is the mapping value of the wear state feature W of the tool, p is the number of wear state features, θ j is the first adjustment factor of the jth wear state characteristic, ρ′ j is the second adjustment factor of the jth wear state characteristic, τ j is the third adjustment factor of the jth wear state characteristic, W j is the jth wear state characteristic, σ′ j is the fourth adjustment factor of the jth wear state characteristic, ω jis the fifth adjustment factor of the jth wear state characteristic, t is time, ζ j is the sixth adjustment factor of the jth wear state characteristic, χ j is the seventh adjustment factor of the jth wear state characteristic.

[0080] Specifically, all adjustment factors are fitted by gradient descent method or ant colony algorithm.

[0081] Step 103, matching the mapping values ​​of the shape features of the knife tool of the knife tool image to be identified, the mapping values ​​of the material properties of the knife tool, and the mapping values ​​of the wear state features of the knife tool with existing knife tools in a database, and finding the best matching knife tool in the database as the recognition result of the knife tool image to be identified.

[0082] For example, the database is a database in a security inspection system of an airport, a railway station, etc., which stores pictures and related data of various knife tools.

[0083] Example 2

[0084] like Figure 2 As shown, the embodiment of the present invention also provides a tool automatic identification and management system based on image recognition, including:

[0085] A feature acquisition module is used to acquire a knife tool image to be identified and extract multiple features of the knife tool image to be identified, wherein the multiple features include: shape features of the knife tool, material properties of the knife tool and wear state features of the knife tool;

[0086] For example, 1. The shape feature vector S mainly describes the geometric shape of the tool and may include the following features:

[0087] S1-Blade length: The blade length of the tool, usually in millimeters (mm).

[0088] S2-Blade Angle: The angle of the cutting edge of a tool, usually measured in degrees (°).

[0089] S3-Tool Width: The width of the tool, usually in millimeters (mm).

[0090] S4-Tool Thickness: The thickness of the tool, usually in millimeters (mm).

[0091] S5-Blade Curvature Radius: The radius of curvature of the blade, usually measured in millimeters (mm).

[0092] S6-Edge shape parameters: Parameters used to describe the shape of the tool edge, such as sharpness or shape factor.

[0093] 2. The material property vector M describes the material properties of the tool and may include the following characteristics:

[0094] M1-Hardness: The hardness of the tool material, such as Vickers hardness (HV) or Rockwell hardness (HRC).

[0095] M2 - Modulus of elasticity: The modulus of elasticity of a material, usually expressed in GPa.

[0096] M3-Density: The density of the material, usually expressed in grams per cubic centimeter (g / cm 3 ) as the unit.

[0097] M4- Thermal conductivity: The thermal conductivity of the material, usually in W / m·K.

[0098] M5-Toughness: The toughness of a material is usually measured by fracture toughness (K_IC), with the unit of MPa·√m.

[0099] M6-Chemical composition: The chemical composition ratio of the material, such as the percentage of carbon content or other alloying elements.

[0100] 3. The wear state vector W describes the wear of the tool and may include the following characteristics:

[0101] W1-Edge wear: The wear of the tool edge, usually in microns (μm).

[0102] W2-Degree of cutting edge chipping: The severity of cutting edge chipping may be a dimensionless quantitative value.

[0103] W3-Surface roughness: The roughness of the tool surface after wear, usually in microns (μm).

[0104] W4- Wear area: The total area of ​​tool surface wear, usually in square millimeters (mm 2 ) as the unit.

[0105] W5- Thermal damage index: The degree of damage to the tool due to overheating, which can be an empirical value or a dimensionless parameter.

[0106] W6-Chip adhesion degree: The degree of chip adhesion to the tool surface, usually expressed as a ratio.

[0107] a mapping value calculation module, used to respectively set a shape feature mapping function, a material property mapping function and a wear state mapping function, and calculate a mapping value of the shape feature of the knife tool, a mapping value of the material property of the knife tool and a mapping value of the wear state characteristic of the knife tool according to the shape feature of the knife tool, the material property of the knife tool and the wear state characteristic of the knife tool;

[0108] Specifically, the shape feature mapping function used to describe the shape change of the knife tool includes:

[0109]

[0110] Among them, f(S) is the mapping value of the shape feature S of the tool, m is the number of shape features, α i is the first adjustment factor of the i-th shape feature, β i is the second adjustment factor of the i-th shape feature, S i is the i-th shape feature, γ i is the third adjustment factor of the i-th shape feature, δ i is the fourth adjustment factor of the i-th shape feature, ∈ i is the fifth adjustment factor of the i-th shape feature, ξ i is the sixth adjustment factor of the i-th shape feature, η ij To control the i-th shape feature S i and the j-th shape feature S j The adjustment factor for the interaction between j is the adjustment factor of the j-th shape feature.

[0111] Specifically, the mapping function of the material properties is used to describe the material properties of the tool and includes:

[0112]

[0113] Among them, g(M) is the mapping value of the material property M of the tool, n is the number of material properties, λ′ i is the first adjustment factor of the i-th material property, η′ i is the second adjustment factor of the i-th material property, M i is the i-th material property, M j is the jth material property, κ ij To control the i-th material property M i and the jth material property M j The adjustment factor for the interaction between i is the third adjustment factor of the i-th material property, ρ i is the fourth adjustment factor of the i-th material property, σ i is the fifth adjustment factor for the i-th material property.

[0114] Specifically, the wear state mapping function used to describe the wear condition of the tool includes:

[0115]

[0116] Where h(W) is the mapping value of the wear state feature W of the tool, p is the number of wear state features, θ j is the first adjustment factor of the jth wear state characteristic, ρ′ j is the second adjustment factor of the jth wear state characteristic, τ j is the third adjustment factor of the jth wear state characteristic, W j is the jth wear state characteristic, σ′ j is the fourth adjustment factor of the jth wear state characteristic, ω j is the fifth adjustment factor of the jth wear state characteristic, t is time, ζ j is the sixth adjustment factor of the jth wear state characteristic, χ j is the seventh adjustment factor of the hth wear state characteristic.

[0117] Specifically, all adjustment factors are fitted by gradient descent method or ant colony algorithm.

[0118] The recognition module is used to match the mapping values ​​of the shape characteristics of the knife tool of the knife tool image to be recognized, the mapping values ​​of the material properties of the knife tool and the mapping values ​​of the wear state characteristics of the knife tool with the existing knife tools in the database, and find the best matching knife tool in the database as the recognition result of the knife tool image to be recognized.

[0119] For example, the database is a database in a security inspection system of an airport, a railway station, etc., which stores pictures and related data of various knife tools.

[0120] In one example, the above-mentioned automatic knife tool identification and management system based on image recognition can be embedded into the security inspection systems of airports, railway stations, etc., as follows:

[0121] 1. Tracking and management of knife tools

[0122] Entry registration: When knives and tools enter the venue, the security system will automatically record the specific information of the tools, including tool number, tool type, owner, using department, entry time, etc. Knives and tools must register information before entering the venue, and the security system will generate a unique identification code.

[0123] Knife tool identification: After taking pictures of tools with a high-speed scanner, the knife tool is identified through an embedded knife tool automatic identification and management system based on image recognition. If the identified knife tool is a dangerous tool, the system will issue a safety prompt and wait for the manager to confirm whether the dangerous tool is allowed to be used. When the manager confirms that the dangerous tool can be used, the safety prompt will be revoked. Otherwise, the dangerous tool cannot be used, and after the registration is completed, the user will be prompted that it is not approved;

[0124] Real-time location tracking: During the use of tools, the system can monitor the location of tools in real time through RFID tags or GPS positioning technology to ensure that all tools are in the authorized area. If the tool is out of the authorized range, the system will automatically trigger an alarm.

[0125] Usage record: The system records detailed information of each use of the knife tool, including the time of use, user, purpose of use, etc. All usage records will be stored by the system and can be queried later.

[0126] 2. System reminder

[0127] Regular inspection reminder: The system will check the tool at preset time intervals to ensure that the tool is in good condition and avoid safety hazards caused by tool failure.

[0128] Abnormal situation reminder: If the system detects abnormal use of the knife tool or exceeds the authorized scope, the system will immediately send a reminder notification to the relevant management personnel.

[0129] 3. Write-off management

[0130] Tool return write-off: After the tool is used, it must be returned to the designated storage area. The system will automatically write off the tool usage record and update the inventory status.

[0131] Damage or loss handling: If a knife tool is damaged or lost during use, the system will record the relevant information and trigger the write-off procedure, requiring relevant personnel to submit a damage or loss report.

[0132] Tool scrapping and write-off: When a tool reaches the end of its service life or needs to be scrapped for some reason, the system will automatically trigger the scrapping and write-off process, record the tool's scrapping information and update the inventory status.

[0133] 4. Management Description

[0134] Permission control: System administrators can set different levels of permissions to ensure that only authorized personnel can operate the system, including information entry, data query, reminder setting and verification operations.

[0135] Data backup and audit: The system backs up data regularly and provides audit functions to ensure that all operation records are transparent and traceable.

[0136] Training and operation guide: In order to ensure the effective operation of the management system, the system should provide detailed operation guide and regular training to ensure that relevant personnel are proficient in the use of the system.

[0137] Example 3

[0138] The embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the automatic identification and management method of knife tools based on image recognition.

[0139] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0140] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining a knife tool image to be identified, and extracting a plurality of features of the knife tool image to be identified, wherein the plurality of features include: shape features of the knife tool, material properties of the knife tool, and wear state features of the knife tool;

[0141] For example, 1. The shape feature vector S mainly describes the geometric shape of the tool and may include the following features:

[0142] S1-Blade length: The blade length of the tool, usually in millimeters (mm).

[0143] S2-Blade Angle: The angle of the cutting edge of a tool, usually measured in degrees (°).

[0144] S3-Tool Width: The width of the tool, usually in millimeters (mm).

[0145] S4-Tool Thickness: The thickness of the tool, usually in millimeters (mm).

[0146] S5-Blade Curvature Radius: The radius of curvature of the blade, usually measured in millimeters (mm).

[0147] S6-Edge shape parameters: Parameters used to describe the shape of the tool edge, such as sharpness or shape factor.

[0148] 2. The material property vector M describes the material properties of the tool and may include the following characteristics:

[0149] M1-Hardness: The hardness of the tool material, such as Vickers hardness (HV) or Rockwell hardness (HRC).

[0150] M2 - Modulus of elasticity: The modulus of elasticity of a material, usually expressed in GPa.

[0151] M3-Density: The density of the material, usually expressed in grams per cubic centimeter (g / cm 3 ) as the unit.

[0152] M4- Thermal conductivity: The thermal conductivity of the material, usually in W / m·K.

[0153] M5-Toughness: The toughness of a material is usually measured by fracture toughness (K_IC), with the unit of MPa·√m.

[0154] M6-Chemical composition: The chemical composition ratio of the material, such as the percentage of carbon content or other alloying elements.

[0155] 3. The wear state vector W describes the wear of the tool and may include the following characteristics:

[0156] W1-Edge wear: The wear of the tool edge, usually in microns (μm).

[0157] W2-Degree of cutting edge chipping: The severity of cutting edge chipping may be a dimensionless quantitative value.

[0158] W3-Surface roughness: The roughness of the tool surface after wear, usually in microns (μm).

[0159] W4- Wear area: The total area of ​​tool surface wear, usually in square millimeters (mm 2 ) as the unit.

[0160] W5- Thermal damage index: The degree of damage to the tool due to overheating, which can be an empirical value or a dimensionless parameter.

[0161] W6-Chip adhesion degree: The degree of chip adhesion to the tool surface, usually expressed as a ratio.

[0162] Step 102, respectively setting a shape feature mapping function, a material property mapping function and a wear state mapping function, and calculating a mapping value of the shape feature of the knife tool, a mapping value of the material property of the knife tool and a mapping value of the wear state characteristic of the knife tool according to the shape feature of the knife tool, the material property of the knife tool and the wear state characteristic of the knife tool;

[0163] Specifically, the shape feature mapping function used to describe the shape change of the knife tool includes:

[0164]

[0165] Among them, f(S) is the mapping value of the shape feature S of the tool, m is the number of shape features, α i is the first adjustment factor of the i-th shape feature, β i is the second adjustment factor of the i-th shape feature, S i is the i-th shape feature, γ i is the third adjustment factor of the i-th shape feature, δ i is the fourth adjustment factor of the i-th shape feature, ∈ i is the fifth adjustment factor of the i-th shape feature, ξi is the sixth adjustment factor of the i-th shape feature, η ij To control the i-th shape feature S i and the j-th shape feature S j The adjustment factor for the interaction between j is the adjustment factor of the j-th shape feature.

[0166] Specifically, the mapping function of the material properties is used to describe the material properties of the tool and includes:

[0167]

[0168] Among them, g(M) is the mapping value of the material property M of the tool, n is the number of material properties, λ′ i is the first adjustment factor of the i-th material property, η′ i is the second adjustment factor of the i-th material property, M i is the i-th material property, M j is the jth material property, κ ij To control the i-th material property M i and the jth material property M j The adjustment factor for the interaction between i is the third adjustment factor of the i-th material property, ρ i is the fourth adjustment factor of the i-th material property, σ i is the fifth adjustment factor for the i-th material property.

[0169] Specifically, the wear state mapping function used to describe the wear condition of the tool includes:

[0170]

[0171] Where h(W) is the mapping value of the wear state feature W of the tool, p is the number of wear state features, θ j is the first adjustment factor of the jth wear state characteristic, ρ′ j is the second adjustment factor of the jth wear state characteristic, τ j is the third adjustment factor of the jth wear state characteristic, W j is the jth wear state characteristic, σ′ j is the fourth adjustment factor of the jth wear state characteristic, ω j is the fifth adjustment factor of the jth wear state characteristic, t is time, ζ j is the sixth adjustment factor of the jth wear state characteristic, x j is the seventh adjustment factor of the jth wear state characteristic.

[0172] Specifically, all adjustment factors are fitted by gradient descent method or ant colony algorithm.

[0173] Step 103, matching the mapping values ​​of the shape features of the knife tool of the knife tool image to be identified, the mapping values ​​of the material properties of the knife tool, and the mapping values ​​of the wear state features of the knife tool with existing knife tools in a database, and finding the best matching knife tool in the database as the recognition result of the knife tool image to be identified.

[0174] For example, the database is a database in a security inspection system of an airport, a railway station, etc., which stores pictures and related data of various knife tools.

[0175] Example 4

[0176] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the automatic identification and management method of knife tools based on image recognition.

[0177] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0178] Among them, the storage medium can be used to store software programs and modules, such as a knife tool automatic identification and management method based on image recognition in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned knife tool automatic identification and management method based on image recognition. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0179] The processor can call the information and application program stored in the storage medium through the transmission system to perform the following steps: Step 101, obtaining a knife tool image to be identified, and extracting multiple features of the knife tool image to be identified, wherein the multiple features include: shape features of the knife tool, material properties of the knife tool, and wear state features of the knife tool;

[0180] For example, 1. The shape feature vector S mainly describes the geometric shape of the tool and may include the following features:

[0181] S1-Blade length: The blade length of the tool, usually in millimeters (mm).

[0182] S2-Blade Angle: The angle of the cutting edge of a tool, usually measured in degrees (°).

[0183] S3-Tool Width: The width of the tool, usually in millimeters (mm).

[0184] S4-Tool Thickness: The thickness of the tool, usually in millimeters (mm).

[0185] S5-Blade Curvature Radius: The radius of curvature of the blade, usually measured in millimeters (mm).

[0186] S6-Edge shape parameters: Parameters used to describe the shape of the tool edge, such as sharpness or shape factor.

[0187] 2. The material property vector M describes the material properties of the tool and may include the following characteristics:

[0188] M1-Hardness: The hardness of the tool material, such as Vickers hardness (HV) or Rockwell hardness (HRC).

[0189] M2 - Modulus of elasticity: The modulus of elasticity of a material, usually expressed in GPa.

[0190] M3-Density: The density of the material, usually expressed in grams per cubic centimeter (g / cm 3 ) as the unit.

[0191] M4- Thermal conductivity: The thermal conductivity of the material, usually in W / m·K.

[0192] M5-Toughness: The toughness of a material is usually measured by fracture toughness (K_IC), with the unit of MPa·√m.

[0193] M6-Chemical composition: The chemical composition ratio of the material, such as the percentage of carbon content or other alloying elements.

[0194] 3. The wear state vector W describes the wear of the tool and may include the following characteristics:

[0195] W1-Edge wear: The wear of the tool edge, usually in microns (μm).

[0196] W2-Degree of cutting edge chipping: The severity of cutting edge chipping may be a dimensionless quantitative value.

[0197] W3-Surface roughness: The roughness of the tool surface after wear, usually in microns (μm).

[0198] W4- Wear area: The total area of ​​tool surface wear, usually in square millimeters (mm 2 ) as the unit.

[0199] W5- Thermal damage index: The degree of damage to the tool due to overheating, which can be an empirical value or a dimensionless parameter.

[0200] W6-Chip adhesion degree: The degree of chip adhesion to the tool surface, usually expressed as a ratio.

[0201] Step 102, respectively setting a shape feature mapping function, a material property mapping function and a wear state mapping function, and calculating a mapping value of the shape feature of the knife tool, a mapping value of the material property of the knife tool and a mapping value of the wear state characteristic of the knife tool according to the shape feature of the knife tool, the material property of the knife tool and the wear state characteristic of the knife tool;

[0202] Specifically, the shape feature mapping function used to describe the shape change of the knife tool includes:

[0203]

[0204] Among them, f(S) is the mapping value of the shape feature S of the tool, m is the number of shape features, α i is the first adjustment factor of the i-th shape feature, β i is the second adjustment factor of the i-th shape feature, S i is the i-th shape feature, γ i is the third adjustment factor of the i-th shape feature, δ i is the fourth adjustment factor of the i-th shape feature, ∈ i is the fifth adjustment factor of the i-th shape feature, ξ i is the sixth adjustment factor of the i-th shape feature, η ij To control the i-th shape feature S i and the j-th shape feature S j The adjustment factor for the interaction between j is the adjustment factor of the j-th shape feature.

[0205] Specifically, the mapping function of the material properties is used to describe the material properties of the tool and includes:

[0206]

[0207] Among them, g(M) is the mapping value of the material property M of the tool, n is the number of material properties, λ′ i is the first adjustment factor of the i-th material property, η′ i is the second adjustment factor of the i-th material property, M iis the i-th material property, M j is the jth material property, κ ij To control the i-th material property M i and the jth material property M j The adjustment factor for the interaction between i is the third adjustment factor of the i-th material property, ρ i is the fourth adjustment factor of the i-th material property, σ i is the fifth adjustment factor for the i-th material property.

[0208] Specifically, the wear state mapping function used to describe the wear condition of the tool includes:

[0209]

[0210] Where h(W) is the mapping value of the wear state feature W of the tool, p is the number of wear state features, θ j is the first adjustment factor of the jth wear state characteristic, ρ′ j is the second adjustment factor of the jth wear state characteristic, τ j is the third adjustment factor of the jth wear state characteristic, W j is the jth wear state characteristic, σ′ j is the fourth adjustment factor of the jth wear state characteristic, ω j is the fifth adjustment factor of the jth wear state characteristic, t is time, ζ j is the sixth adjustment factor of the jth wear state characteristic, χ j is the seventh adjustment factor of the jth wear state characteristic.

[0211] Specifically, all adjustment factors are fitted by gradient descent method or ant colony algorithm.

[0212] Step 103, matching the mapping values ​​of the shape features of the knife tool of the knife tool image to be identified, the mapping values ​​of the material properties of the knife tool, and the mapping values ​​of the wear state features of the knife tool with existing knife tools in a database, and finding the best matching knife tool in the database as the recognition result of the knife tool image to be identified.

[0213] For example, the database is a database in a security inspection system of an airport, a railway station, etc., which stores pictures and related data of various knife tools.

[0214] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0215] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0216] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0217] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0218] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0219] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0220] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for automatic identification and management of knife tools based on image recognition, characterized in that: include: Acquire a knife tool image to be identified, and extract multiple features of the knife tool image to be identified, wherein the multiple features include: shape features of the knife tool, material properties of the knife tool, and wear state features of the knife tool; Respectively setting a shape feature mapping function, a material property mapping function and a wear state mapping function, and calculating a mapping value of the shape feature of the knife tool, a mapping value of the material property of the knife tool and a mapping value of the wear state characteristic of the knife tool according to the shape feature of the knife tool, the material property of the knife tool and the wear state characteristic of the knife tool; The mapping values ​​of the shape features of the knife tool in the knife tool image to be identified, the mapping values ​​of the material properties of the knife tool and the mapping values ​​of the wear state features of the knife tool are matched with the knife tools already in the database, and the best matching knife tool in the database is found as the recognition result of the knife tool image to be identified.

2. The method for automatic identification and management of knife tools based on image recognition according to claim 1, characterized in that: The shape feature mapping function used to describe the shape change of the knife tool includes: Among them, f(S) is the mapping value of the shape feature S of the tool, m is the number of shape features, α i is the first adjustment factor of the i-th shape feature, β i is the second adjustment factor of the i-th shape feature, S i is the i-th shape feature, γ i is the third adjustment factor of the i-th shape feature, δ i is the fourth adjustment factor of the i-th shape feature, ∈ i is the fifth adjustment factor of the i-th shape feature, ξ i is the sixth adjustment factor of the i-th shape feature, η ij To control the i-th shape feature S i and the j-th shape feature S j The adjustment factor for the interaction between j is the adjustment factor of the j-th shape feature.

3. The automatic identification and management method for knife tools based on image recognition as claimed in claim 1, characterized in that: The mapping function of the material properties used to describe the material properties of the knife tool includes: Among them, g(M) is the mapping value of the material property M of the tool, n is the number of material properties, λ′ i is the first adjustment factor of the i-th material property, η′ i is the second adjustment factor of the i-th material property, M i is the i-th material property, M j is the jth material property, κ ij To control the i-th material property M i and the jth material property M j The adjustment factor for the interaction between i is the third adjustment factor of the i-th material property, ρ i is the fourth adjustment factor of the i-th material property, σ i is the fifth adjustment factor for the i-th material property.

4. The method for automatic identification and management of knife tools based on image recognition as claimed in claim 1, characterized in that: The wear state mapping function used to describe the wear condition of the tool includes: Where h(W) is the mapping value of the wear state feature W of the tool, p is the number of wear state features, θ j is the first adjustment factor of the jth wear state characteristic, ρ′ j is the second adjustment factor of the jth wear state characteristic, τ j is the third adjustment factor of the jth wear state characteristic, W j is the jth wear state characteristic, σ′ j is the fourth adjustment factor of the jth wear state characteristic, ω j is the fifth adjustment factor of the jth wear state characteristic, t is time, ζ j is the sixth adjustment factor of the jth wear state characteristic, χ j is the seventh adjustment factor of the jth wear state characteristic.

5. The automatic identification and management method for knife tools based on image recognition as claimed in any one of claims 2, 3 or 4, characterized in that: All adjustment factors are fitted by gradient descent method or ant colony algorithm.

6. An automatic identification and management system for knife tools based on image recognition, characterized in that: include: A feature acquisition module is used to acquire a knife tool image to be identified and extract multiple features of the knife tool image to be identified, wherein the multiple features include: shape features of the knife tool, material properties of the knife tool and wear state features of the knife tool; a mapping value calculation module, used to respectively set a shape feature mapping function, a material property mapping function and a wear state mapping function, and calculate a mapping value of the shape feature of the knife tool, a mapping value of the material property of the knife tool and a mapping value of the wear state characteristic of the knife tool according to the shape feature of the knife tool, the material property of the knife tool and the wear state characteristic of the knife tool; The recognition module is used to match the mapping values ​​of the shape characteristics of the knife tool of the knife tool image to be recognized, the mapping values ​​of the material properties of the knife tool and the mapping values ​​of the wear state characteristics of the knife tool with the knife tools already in the database, and find the most matching tool in the database as the recognition result of the knife tool image to be recognized.

7. The automatic knife tool identification and management system based on image recognition as claimed in claim 6, characterized in that: The shape feature mapping function used to describe the shape change of the knife tool includes: Among them, f(S) is the mapping value of the shape feature S of the tool, m is the number of shape features, α i is the first adjustment factor of the i-th shape feature, β i is the second adjustment factor of the i-th shape feature, S i is the i-th shape feature, γ i is the third adjustment factor of the i-th shape feature, δ i is the fourth adjustment factor of the i-th shape feature, ∈ i is the fifth adjustment factor of the i-th shape feature, ξ i is the sixth adjustment factor of the i-th shape feature, η ij To control the i-th shape feature S i and the j-th shape feature S j The adjustment factor for the interaction between j is the adjustment factor of the j-th shape feature.

8. The automatic knife tool identification and management system based on image recognition as claimed in claim 6, characterized in that: The mapping function of the material properties used to describe the material properties of the knife tool includes: Among them, g(M) is the mapping value of the material property M of the tool, n is the number of material properties, λ′ i is the first adjustment factor of the i-th material property, η′ i is the second adjustment factor of the i-th material property, M i is the i-th material property, M j is the jth material property, κ ij To control the i-th material property M i and the jth material property M j The adjustment factor for the interaction between i is the third adjustment factor of the i-th material property, ρ i is the fourth adjustment factor of the i-th material property, σ i is the fifth adjustment factor for the i-th material property.

9. The automatic knife tool identification and management system based on image recognition as claimed in claim 6, characterized in that: The wear state mapping function used to describe the wear condition of the tool includes: Where h(W) is the mapping value of the wear state feature W of the tool, p is the number of wear state features, θ j is the first adjustment factor of the jth wear state characteristic, ρ′ j is the second adjustment factor of the jth wear state characteristic, τ j is the third adjustment factor of the jth wear state characteristic, W j is the jth wear state characteristic, σ′ j is the fourth adjustment factor of the jth wear state characteristic, ω j is the fifth adjustment factor of the jth wear state characteristic, t is time, ζ j is the sixth adjustment factor of the jth wear state characteristic, χ j is the seventh adjustment factor of the jth wear state characteristic.

10. The automatic knife tool identification and management system based on image recognition as claimed in any one of claims 7, 8 or 9, characterized in that: All adjustment factors are fitted by gradient descent method or ant colony algorithm.

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